Performing noise cancellation on a memory device using a neural network

Abstract

A memory system includes a memory device, and a memory controller including a processor and an internal memory. A computer program including a neural network is stored in the memory system. The processor executes the computer program to extract a voltage level from each of a plurality of memory cells connected to one string select line (SSL), in which the memory cells and the SSL are included in a memory block of the memory device, provide the voltage levels as input to the neural network, and perform noise cancellation on the SSL, using the neural network, by changing at least one of the voltage levels from a first voltage level to a second voltage level. The first voltage level is classified into a first cluster of memory cells, and the second voltage level is classified into a second cluster of memory cells different from the first cluster.

What is claimed is: 
     
       1. A memory system, comprising:
 a memory device; and 
 a memory controller comprising a processor and an internal memory, 
 wherein the memory device operates under control of the memory controller, and a computer program comprising a neural network is stored in the internal memory of the memory controller or the memory device; 
 wherein the processor is configured to execute the computer program to: 
 (a) extract a voltage level from each of a plurality of memory cells connected to one string select line (SSL), wherein the memory cells and the SSL are included in a memory block of the memory device; 
 (b) provide the voltage levels of the memory cells as input to the neural network; and 
 (c) perform noise cancellation on the SSL, using the neural network, by changing a voltage level of an errant memory cell among the memory cells that is misclassified as a first cluster of the memory cells from a first voltage level corresponding to the first cluster to a second voltage level corresponding to a second cluster of the memory cells different from the first cluster. 
 
     
     
       2. The memory system of  claim 1 , wherein the processor is further configured to execute the computer program to:
 perform an effectiveness check during a training mode of the neural network, wherein performing the effectiveness check comprises: 
 comparing a bit error rate (BER) of each of the memory cells before performing noise cancellation on the SSL with a BER of each of the memory cells after performing noise cancellation on the SSL. 
 
     
     
       3. The memory system of  claim 2 , wherein the processor is further configured to execute the computer program to:
 perform a level skip operation during an inference mode of the neural network, wherein performing the level skip operation comprises: 
 identifying at least one memory cell for which the BER is not improved after performing noise cancellation on the SSL, based on the effectiveness check; and 
 reverting a corresponding voltage level of the identified at least one memory cell to a value that the identified at least one memory cell had before performing noise cancellation on the SSL. 
 
     
     
       4. The memory system of  claim 1 , wherein the processor is further configured to execute the computer program to:
 perform data normalization on the voltage levels of the memory cells before noise cancellation has been performed on the SSL; and 
 perform data de-normalization on the voltage levels of the memory cells after noise cancellation has been performed on the SSL. 
 
     
     
       5. The memory system of  claim 1 , wherein the processor is further configured to execute the computer program to:
 perform voltage level mean correction on the voltage levels of the memory cells before noise cancellation has been performed on the SSL. 
 
     
     
       6. The memory system of  claim 1 , wherein a number of the extracted voltage levels corresponds to a number of word lines connected to the SSL. 
     
     
       7. The memory system of  claim 1 , wherein the processor executes the computer program to perform operations (a) to (c) when the memory block is being read during a read operation of the memory device. 
     
     
       8. The memory system of  claim 1 , wherein each memory cell stores 6 bits, each memory cell has one of 64 possible states corresponding to 64 predefined voltage levels, and the SSL included in the memory block is one of four SSLs included in the memory block. 
     
     
       9. The memory system of  claim 8 , wherein the processor executes the computer program to perform operations (a) through (c) independently on each of the four SSLs included in the memory block. 
     
     
       10. The memory system of  claim 8 , wherein the first and second clusters are included among 64 clusters corresponding to the 64 predefined voltage levels. 
     
     
       11. The memory system of  claim 1 , wherein the neural network is stored on and executed on the memory device. 
     
     
       12. The memory system of  claim 1 , wherein the neural network is a residual neural network (ResNet). 
     
     
       13. The memory system of  claim 1 , wherein the neural network comprises:
 an input layer having a size corresponding to a number of the extracted voltage levels; and 
 an output layer having a size corresponding to the number of the extracted voltage levels, 
 wherein each layer in the neural network is a fully connected layer. 
 
     
     
       14. A method of performing noise cancellation on a memory device using a neural network, comprising:
 (a) extracting a voltage level from each of a plurality of memory cells connected to one string select line (SSL), wherein the memory cells and the SSL are included in a memory block of the memory device; 
 (b) providing the voltage levels of the memory cells as input to the neural network; and 
 (c) performing noise cancellation on the SSL, using the neural network, by changing a voltage level of an errant memory cell among the memory cells that is misclassified as a first cluster of the memory cells from a first voltage level corresponding to the first cluster to a second voltage level corresponding to a second cluster of the memory cells different from the first cluster. 
 
     
     
       15. The method of  claim 14 , further comprising:
 performing an effectiveness check during a training mode of the neural network, wherein performing the effectiveness check comprises: 
 comparing a bit error rate (BER) of each of the memory cells before performing noise cancellation on the SSL with a BER of each of the memory cells after performing noise cancellation on the SSL. 
 
     
     
       16. The method of  claim 15 , further comprising:
 performing a level skip operation during an inference mode of the neural network, wherein performing the level skip operation comprises: 
 identifying at least one memory cell for which the BER is not improved after performing noise cancellation on the SSL, based on the effectiveness check; and 
 reverting a corresponding voltage level of the identified at least one memory cell to a value that the identified at least one memory cell had before performing noise cancellation on the SSL. 
 
     
     
       17. The method of  claim 14 , further comprising:
 performing data normalization on the voltage levels of the memory cells before performing noise cancellation on the SSL; and 
 performing data de-normalization on the voltage levels of the memory cells after performing noise cancellation on the SSL. 
 
     
     
       18. The method of  claim 14 , further comprising:
 performing voltage level mean correction on the voltage levels of the memory cells before performing noise cancellation on the SSL. 
 
     
     
       19. The method of  claim 14 , wherein a number of the extracted voltage levels corresponds to a number of word lines connected to the SSL. 
     
     
       20. The method of  claim 14 , wherein operations (a) to (c) are performed when the memory block is being read during a read operation of the memory device.